Simultaneous remote sensing image classification and annotation based on the spatial coherent topic model

The traditional LDA models to solve the problem of scene classification lack the spatial relationship between the fragments of images or the parts of targets and linkages between the global and local information, so their performance is usually poor in stability for the images with clutter background. In this paper, a novel method for the simultaneous classification and annotation of remote sensing images with complex scenes is proposed. The Spatially Consistent Topic Model is defined by making full use of the correlation between image classification and annotation. We choose SIFT features, hue features and texture features as the visual words, which help to endow pixels of similar appearance region with the same hidden topic. Competitive results on remote sensing images demonstrate the precision and robustness of the proposed method.